update pyramid for ablation
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+52
-65
@@ -11,7 +11,7 @@ parser.add_argument('--debug', action='store_true')
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args = vars(parser.parse_args())
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if args['debug']:
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from configs.iccvw.pyr_64_cape_m import config
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from configs.iccvw.pyr_64_reg import config
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else:
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config_module = importlib.import_module(args['config'])
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config = config_module.config
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@@ -238,72 +238,41 @@ def train_step(batch):
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res_modifier = '_' + str(res) if res != 512 else ''
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pred, mean, logvar = out_levels[i]
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mesh_pred = uv2mesh(pred)
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if config['use_skinning']:
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shape = batch['pose_shape'][:, 72:]
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gender = torch.greater_equal(shape[:, 10] , 0.5).to(dtype=torch.int64)
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shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])])
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mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None]
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mesh_pred_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)[0]
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else:
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mesh_pred_posed = mesh_pred
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# is the output already masked?
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# ms_ssim = torch.mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
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# ms_ssim = torch.mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
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e_loss = torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss, c_ratio = collision_loss(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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c_loss, c_ratio = torch.mean(c_loss), torch.mean(c_ratio)
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l2_loss = torch.mean(1e3 * torch.mean(torch.sum(((uv_orig - pred) ** 2), dim=-1), dim=[1, 2]))
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l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, dim=-1), dim=-1))
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n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
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kl = kl_loss(mean, logvar)
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if config['use_extra_losses']:
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mesh_pred = uv2mesh(pred)
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if config['use_skinning']:
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shape = batch['pose_shape'][:, 72:]
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gender = torch.greater_equal(shape[:, 10] , 0.5).to(dtype=torch.int64)
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shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])])
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mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None]
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mesh_pred_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)[0]
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else:
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mesh_pred_posed = mesh_pred
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e_loss = torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss, c_ratio = collision_loss(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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c_loss, c_ratio = torch.mean(c_loss), torch.mean(c_ratio)
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n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
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l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, dim=-1), dim=-1))
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
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if not config['is_cape']:
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
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if config['use_norm_loss'] and step > 2000:
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reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
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else:
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
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if config['use_norm_loss'] and step > 2000:
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reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
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kl_total += kl
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loss: torch.Tensor = reconstr_loss_total + g_balancing_coeff * kl_total
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# scaler.scale(loss).backward()
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# scaler.step(optimizer)
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# scaler.update()
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# optimizer.zero_grad()
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# b = 0
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# from custom_utils.custom_utils import _maskFaces
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# mesh_mask = batch['mesh_mask'][b].cpu().numpy()
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# faces = np.load('smpl/smpl_faces_skirt.npy') if batch['garment'][b] in ['Skirt', 'Dress'] else np.load('smpl/smpl_faces.npy')
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# faces_body = np.load('smpl/smpl_faces.npy')
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# _, faces = _maskFaces(np.zeros((14475, 3)), faces, mesh_mask)
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# # fig = go.Figure(data=[go.Mesh3d(x=batch['mesh_posed'].cpu().numpy()[0,mesh_mask,0], y=batch['mesh_posed'].cpu().numpy()[0,mesh_mask,2], z=batch['mesh_posed'].cpu().numpy()[0,mesh_mask,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1),
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# # go.Mesh3d(x=batch['mesh_body_posed'].cpu().numpy()[0,:,0], y=batch['mesh_body_posed'].cpu().numpy()[0,:,2], z=batch['mesh_body_posed'].cpu().numpy()[0,:,1], i=faces_body[:,0], j=faces_body[:,1], k=faces_body[:,2], color='lightblue', opacity=1),
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# # ])
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# fig = go.Figure(data=[go.Mesh3d(x=mesh_orig.cpu().numpy()[0,mesh_mask,0], y=mesh_orig.cpu().numpy()[0,mesh_mask,2], z=mesh_orig.cpu().numpy()[0,mesh_mask,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1),
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# ])
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# fig.update_layout(scene_aspectmode='cube')
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# fig.update_layout(scene=dict(xaxis=dict(range=[-1,1]), yaxis=dict(range=[-1,1]), zaxis=dict(range=[-1,1])))
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# fig.show()
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# ex.imshow((uv_orig[b].cpu().numpy().transpose(1,2,0) + 1) / 2).show()
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# upscaled = model.levels[0].enhance(levels_input[0][0], levels_input[0][-2]) # levels_input[i][-2]=uv_mask_res
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# upscaled = model.resizers[512](upscaled)
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# ex.imshow((levels_input[0][0][b].cpu().numpy().transpose(1,2,0) + 1) / 2).show()
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# ex.imshow((upscaled[b].cpu().numpy().transpose(1,2,0) + 1) / 2).show()
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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@@ -313,13 +282,33 @@ def train_step(batch):
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mean = torch.mean(mean)
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logvar = torch.mean(logvar)
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if not config['use_extra_losses']:
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mesh_pred = uv2mesh(pred)
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if config['use_skinning']:
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shape = batch['pose_shape'][:, 72:]
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gender = torch.greater_equal(shape[:, 10] , 0.5).to(dtype=torch.int64)
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shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])])
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mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None]
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mesh_pred_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)[0]
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else:
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mesh_pred_posed = mesh_pred
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e_loss = torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss, c_ratio = collision_loss(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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c_loss, c_ratio = torch.mean(c_loss), torch.mean(c_ratio)
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l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, dim=-1), dim=-1))
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n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
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ms_ssim = torch.mean(_MSSSIM(pred + 1, batch['uv_static'] + 1))
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metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred.detach()) ** 2, axis=-1))) / torch.sum(batch['mesh_mask'])
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output = { **output, **{ 'train/edge_loss' + res_modifier: e_loss, 'train/collision_loss' + res_modifier: c_loss,'train/euclid_error' + res_modifier: metric,
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'train/mean' + res_modifier: mean, 'train/logvar' + res_modifier: logvar, 'train/kl_loss' + res_modifier: kl,
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'train/var' + res_modifier: np.e ** logvar, 'train/l2_loss' + res_modifier: l2_loss, 'train/normal_loss': n_loss,
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'train/euclid_error': metric, 'train/l2_mesh_loss': l2_mesh_loss, 'train/ms_ssim' + res_modifier: ms_ssim, } } #
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'train/euclid_error': metric, 'train/l2_mesh_loss': l2_mesh_loss, 'train/ms_ssim' + res_modifier: ms_ssim,
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'train/collision_ratio': c_ratio } } #
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return { **{ 'train/loss': loss, 'train/reconstr_loss_total': reconstr_loss_total, 'train/kl_total': kl_total }, **output }
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@@ -359,28 +348,24 @@ def valid_step(batch):
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else:
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mesh_pred_posed = mesh_pred
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# is the output already masked?
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# ms_ssim = torch.mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
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# ms_ssim = torch.mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
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e_loss = torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss, c_ratio = collision_loss(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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c_loss, c_ratio = torch.mean(c_loss), torch.mean(c_ratio)
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l2_loss = torch.mean(1e3 * torch.mean(torch.sum(((uv_orig - pred) ** 2), dim=-1), dim=[1, 2]))
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l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, dim=-1), dim=-1))
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n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
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kl = kl_loss(mean, logvar)
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if config['use_extra_losses']:
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
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if not config['is_cape']:
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
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if config['use_norm_loss'] and step > 2000:
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reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
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else:
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
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if config['use_norm_loss'] and step > 2000:
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reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
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kl_total += kl
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loss = reconstr_loss_total + g_balancing_coeff * kl_total
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@@ -825,9 +810,9 @@ def train():
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print(step)
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if train_loss['train/euclid_error'] < config['balancing_activation_limit']:
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g_balancing_coeff += 1 / 10000
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g_balancing_coeff += 1 / 50000
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if step % 25000 == 0:
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if step % 25000 == 0 and not args['debug']:
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ckpt_saver(model, step, optimizer, None)
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step += 1
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@@ -874,12 +859,14 @@ if __name__ == '__main__':
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if len(config['pyramid']['resolutions']) == 1:
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for param in model.static_enc.parameters(): param.requires_grad = False
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for param in model.body_enc.parameters(): param.requires_grad = False
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for param in model.norm_enc.parameters(): param.requires_grad = False
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if config['use_normals']:
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for param in model.norm_enc.parameters(): param.requires_grad = False
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else:
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load_model(model, config['pyramid']['pretrained_pyramid'], iteration=(config['pyramid']['pretrained_pyramid_iteration'] if 'pretrained_pyramid_iteration' in config.keys() else ''))
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load_model(model, config['pyramid']['pretrained_pyramid'], iteration=config['pyramid']['pretrained_pyramid_iteration'])
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for param in model.static_enc.parameters(): param.requires_grad = False
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for param in model.body_enc.parameters(): param.requires_grad = False
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for param in model.norm_enc.parameters(): param.requires_grad = False
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if config['use_normals']:
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for param in model.norm_enc.parameters(): param.requires_grad = False
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# load_model(model.levels[0].upscale_enhancer, config['pyramid']['levels'][0]['upscale_enhancer'])
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# model.levels[0].upscale_enhancer.trainable = False
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